International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
A Noise Reduction Method Based on Modified Least Mean Square Algorithm of Real Time Speech Signals With The Help of Wiener Filter 1Dr S.China Venkateswarlu,2Srijan Verma, 3Kakasani Sai Teja
, Chintha Sudheer 4
1Professor, Institute of Aeronautical Engineering, Hyderabad, Telangana 234Students of Electronics and Communication Engineering, Institute of Aeronautical Engineering, Hyderabad,
Telangana ---------------------------------------------------------------------***--------------------------------------------------------------------
Abstract - Real-time voice denoising employs an
adaptive filtering technique with variable length filters that tracks the noise characteristics and selects the filter equations based on those features. The LMS algorithm's primary benefits are its low computational complexity and evidence of convergence in stationary environments. This research proposes a modified LMS technique for real-time speech signal denoise. The suggested approach increases the capabilities of adaptive filtering by fusing the general LMS algorithm with the diffusion least mean-square algorithm. The suggested algorithm is successful in reducing speech signal noise, according to the calculation of the performance parameter. For replications and additional research applications, a complete MATLAB programming method is given. Key Words: Speech Enhancement, LMS, MATLAB, Modified LMS Algorithm, Segmental SNR, LLR, ISD, Cepstrum, Weiner Filter.
1.INTRODUCTION
The non-variable forgetting factor RLS (NVFFRLS), often known as the RLS algorithm, powers two of the filters, one of order 5 and the other of order 10, while the other two filters, both of order 2, are powered by variable forgetting factor RLS (VFFRLS) algorithm isused to drive two of the orders, one of order 5, and two of order 10. When it comes to RLS, the forgetting factor has a value of =0.99 while it has a value of min=0.95 when it comes to VFFRLS. This is due to the fact that the VFFRLS algorithm can monitor changes in the noisy signal more precisely than the RLS method. The performance of the RLS algorithm with a variable forgetting factor for non-stationary processes has improved, which is consistent with the researchers' findings. The researchers created a brandnew adaptive filtering method called the modified adaptive filtering with averaging (MAFA) algorithm, which is utilized to remove white Gaussian noise from voice samples.
1.1 BLOCK DIAGRAM AND FLOWCHART
Voice transmissions may experience interference from various noise components while being transported via transmission lines before they reach their destinations. If these noise components are not eliminated, the voice signals may degrade to the point where their receiving ends suffer a partial or complete loss of the information content. The elimination of these undesirable components has been addressed by numerous researchers using various adaptive filtering techniques.
In this approach, the algorithm's parameters are improved by adding a weiner filter to the alreadyexisting algorithm. The parameters of the original method and the suggested approach are compared after the Weiner filter has been added. The NOIZEUS sound database is used to source the noise signals. Signals with different strengths, ranging from 0 dB to 15 dB,
In order to minimise noise in speech signals, the authors showed how well the recursive least square (RLS) algorithm performed. They got three noise components machine gun, F16, and speech noises from the NOISE-92 database in addition to a clean voice signal from the Hindi speech database. The sampling frequency and resolution for both the noise and the clean signal components are 16 KHz and 16 bits, respectively. Twelve separate noisy speech signals were created by successively adding each of the three noises to a clean speech signal at signal to noise ratios (SNR) of -5dB, 0dB, 5dB, and 10dB levels. Six specially created filters were each fed a set of noisy signals in order to simulate them. © 2023, IRJET
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